📢 It looks like relative representations are here to stay!
I'm beyond thrilled to announce that our work has been selected as one of the notable top 5% (oral) papers at #iclr23 ! 🥳
x.com/moschella_luca/status/…
[1/5]
Welcome Relative Representations, enabling zero-shot communication between latent spaces without any training!
arxiv.org/abs/2209.15430
It turns out that distinct neural networks learn intrinsically equivalent latent spaces [1/6]
Great to wake up on a lazy Sunday and see REBEL is still doing numbers. This is the page for top seq2seq models at @huggingface, currently top 12 with 200K monthly downloads. Thank you to whomever is using it and honestly curious to know what people are using it for.
machine learning researchers learn to optimise their own best paper rate through collusion and other unregulated mechanisms
reddit.com/r/MachineLearning…
Hey #NLProc, I built this little tool to make working with @huggingface 🤗Transformers a bit easier. If you want to directly access whole-word embeddings hassle-free, give it a try!
👉GitHub: github.com/Riccorl/transform…
#acl2022 nlp Best Resource Paper
DiBiMT: A Novel Benchmark for Measuring Word Sense Disambiguation Biases in Machine Translation
(Niccolò Campolungo, Federico Martelli, Francesco Saina and Roberto Navigli)
#acl2022#NLProc
🥳 We are proud to share the news that our DiBiMT paper on Disambiguation Biases in MT received the ✨Best Resource Paper Award✨ @aclmeeting#ACL2022! See you there!
- (neural) models are pretty confident of their incorrect translations: >90% of the time, they deem their BAD❌ translation better than a GOOD ✅ one!
- In neural Transformer models, disambiguation does not seem to be happening on the encoder side 🤔
6/7
We will present DiBiMT on the 25th, during the Best Paper Oral Session at 14.45 Dublin time!
Or, you can come have a chat during Poster Session 6: Resources and Evaluation on the same day, from 10:45 to 12:15! Come say hi 👋😄
7/7